APPLICATIONS

REvolution Computing, the leading commercial provider of software and support for the R open source statistical computing language, today announced it has launched ParallelR version 1.2. ParallelR enables R users to complete statistical analyses much more quickly by taking full advantage of multiprocessor systems and network systems. "Drawing on our deep experience with parallel processing, we have designed a product with unmatched flexibility and capability," said Richard Schultz, CEO, REvolution Computing, Inc. "With ParallelR, users can run analyses on any parallel computing resource from a dual processor laptop to a network cluster to an intercontinental grid. Commercial R users will have a faster, more efficient experience with ParallelR," Schultz added. ParallelR dramatically reduces time to results by utilizing all available computing resources. Computations can be run across heterogeneous hardware configurations, including 32 bit, 64 bit, SMP, and cross-platform configurations, such as combinations of Linux, Windows, and Mac OS X operating systems. An easy installation process reduces IT time and dependency, and transparent integration with enterprise scheduling systems simplifies the management of shared computing tasks. If assistance is needed, REvolution's full enterprise-level customer support is available. A consistent programming paradigm for parallelized analyses increases productivity for the analyst and programmer. ParallelR enhances parallel computing in R for users with a range of goals:

• For the analyst who wants results quickly without extra R programming, ParallelR provides ready-to-run parallel implementations of key R routines.
• For the R programmer who wants to quickly adapt specialized analyses containing time-independent subtasks to run on multiple processes, ParallelR provides the tools to easily wrap R scripts and general R routines into parallel routines. Distribution and load-balancing will be handled automatically.
• For the experienced R programmer who wants to fine tune and optimize parallelized R code, ParallelR provides direct access to a full parallelization toolkit. This toolkit provides a complete framework for coordinating programs written in R by creating a shared, network-based R environment which looks and feels like a conventional R environment. To assist in developing and optimizing your analysis, the network monitor allows you to examine values in your network workspace via a web interface.

ParallelR Version 1.2 was officially launched at the 2008 useR! Conference in Germany. To learn more about ParallelR, please visit www.revolution-computing.com.